Python basics for summer school-2023, UEF

Table of Contents

In this course, we will show you basic syntax of python and some usefull packages regard to data science.

What is Python?

• High-level, interpreted programming language.
• Known for its simplicity and readability.
• Used for web development, data analysis, artificial intelligence, and more.

What is Python?

Python is a user-frendly language

For exammple, output Hello, World! in C# and Python.

namespace ConsoleApp1
{
    internal class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Hello, World!");
        }
    }
}

print("Hello, World!")

Basic Python syntax

One biggest difference of syntax between Python and other languages is that:

• Python uses indentation rather than’{}’ to control functions or loops or conditioanl statement.

🙅‍♂️

if 5 > 2 {
  print("Five is greater than two!")
}

Basic Python syntax

One biggest difference of syntax between Python and other languages is that:

• Python uses indentation rather than’{}’ to control functions or loops or conditioanl statement.

🙆‍♂️

if 5 > 2:
  print("Five is greater than two!")

Basic Python syntax

One biggest difference of syntax between Python and other languages is that:

• Python uses indentation rather than ‘{}’ to control functions or loops or conditioanl statement.

if 5 > 2 {
  print("Five is greater than two!")
}

if 5 > 2:
  print("Five is greater than two!")

How about we remove the indentation of above code:

if 5 > 2:
print("Five is greater than two!")

IndentationError: expected an indented block after 'if' statement on line 1 (2406224367.py, line 2)

Basic Python syntax

Variables

• left is variable name, right is value.

a = 1
b = 3.1
c = "Teemo"

# or you can assign variables just like follow:

a,b,c = 1,3.1,"Teemo"
print(a)
print(b)
print(c)

1
3.1
Teemo

• Always give a value when you start to create a variable

d
print(d)

NameError: name 'd' is not defined

'11'

Basic Python syntax

Variables

• It is possible for converting data of one type to another.
  • Implicit Conversion - automatic type conversion
  • Explicit Conversion - manual type conversion

For example:

# Implicit Conversion
a = 1
print(f"a is {type(a)}")

b = 1.0
print(f"b is {type(b)}")

c = a+b
print(f"c is {type(c)}")

a is <class 'int'>
b is <class 'float'>
c is <class 'float'>

Basic Python syntax

Variables

• It is possible for converting data of one type to another.
  • Implicit Conversion - automatic type conversion
  • Explicit Conversion - manual type conversion

For example:

# Explicit Conversion
a = 1
print(f"a is {type(a)}")

b = "python"
print(f"b is {type(b)}")

c = a+b
print(f"c is {type(c)}")

a is <class 'int'>
b is <class 'str'>

TypeError: unsupported operand type(s) for +: 'int' and 'str'

c = str(a) + b 
print(f"{c} is {type(c)}")

1python is <class 'str'>

Basic Python syntax

Variables —- list

• Elements can be mixed with different types

list1 = [1, "2", True, 4]

• Element can be selected by position index (from 0)

list1 = [1, "2", True, 4]
print(list1[1])

2

Basic Python syntax

Variables —- dict

• Dictionaries are used to store data values in key:value pairs.
  • ordered
  • changeable
  • not allow duplicates

phone = {
  "brand": "Nokia",
  "model": "N95",
  "year": 2006
}

Basic Python syntax

Variables —- dict

To get keys or values of a dict:

• directly call the key

phone = {
  "brand": "Nokia",
  "model": "N95",
  "year": 2006
}

print(phone['model'])
#or
print(phone.get('model'))

N95
N95

#get values
print(phone.values())
#get keys
print(phone.keys())
# or both
print(phone.items())

dict_values(['Nokia', 'N95', 2006])
dict_keys(['brand', 'model', 'year'])
dict_items([('brand', 'Nokia'), ('model', 'N95'), ('year', 2006)])

Basic Python syntax

Basic arithmetic operations

Control Flow Statements

Conditional statements

If we want to try multi-conditions, elis and else keywords are needed:

a = 33
b = 33
if b > a:
    print("b is greater than a")
elif a == b:
    print("a and b are equal")
else:
    print("b is less than a")

a and b are equal

Control Flow Statements

Loops

• for loop
• while loop
• Both for and while loops can be linked to:
  • break statement
    • stop the loop before it has looped through all the items
  • continue statement
    • stop current iteration, move to next
  • else statement
    • to specifies codes to be executed when loop is ended

Control Flow Statements

for loop

numbers = [1,2,3,4]
for x in numbers:
    if x == 1:
        continue
    if x == 3:
        continue
    print(x)
else:
    print("loos is over")

2
4
loos is over

with break:

numbers = [1,2,3,4]
for x in numbers:
    if x == 2:
      break
    print(x)

1

Control Flow Statements

while loop

• Endless execute a set of statements as long as a condition is true.

i = 1
while i < 6:
  print(i)
  i += 1

• can also work with break and continue

# here 5 is omitted
i = 0
while i < 6:
    i = i + 1
    if i == 3:
        continue
    if i == 4:
        break
    print(i)

1
2

Function

Define and call function

• The keyword def is used to define function

def say_hello():
    print("hello world!!")

• Use the function name to call

# define a function
def say_hello():
    print("hello world!!")

# call the function
say_hello()

hello world!!

• pass parameter

def say_hello(name):
    print(name+" hello world!!")
    
say_hello("Teemo")

Teemo hello world!!

Usefull modules

Numpy

Numpy is an important module for scientific calculation

conda install numpy

pip install numpy

import numpy as np

for (i = 0; i < rows; i++) {
  for (j = 0; j < columns; j++) {
    c[i][j] = a[i][j]*b[i][j];
  }
}

c = a * b

Usefull modules

Numpy

• Creating arrays

import numpy as np

# 1-dimensional array
arr1d = np.array([1, 2, 3, 4, 5])

# 2-dimensional array
arr2d = np.array([[1, 2, 3], [4, 5, 6]])

• Array shape and dimensions

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr.shape)  # (2, 3)
print(arr.ndim)   # 2

Usefull modules

Numpy

• Basic array operation

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(f"origin: \n {arr}")

# Reshape the array to (3, 2)
reshaped_arr = arr.reshape((3, 2))
print(f"reshaped: \n  {reshaped_arr}")
# Transpose the array
transposed_arr = arr.T
print(f"transposed: \n {transposed_arr}")

origin: 
 [[1 2 3]
 [4 5 6]]
reshaped: 
  [[1 2]
 [3 4]
 [5 6]]
transposed: 
 [[1 4]
 [2 5]
 [3 6]]

• More detail please read Numpy document

Usefull modeles

Pandas

• Pandas VS. Build-in IO

import pandas as pd

# Read data.txt using Pandas
df = pd.read_csv('data.txt')

with open('data.txt', 'r') as file:
    # Skip the header line
    next(file)
    # Initialize an empty list to store data
    data = []
    # Read and process each line in the file
    for line in file:
        line = line.strip()  # Remove leading/trailing whitespaces
        col1, col2, col3 = line.split(',')  # Split the line using comma as the delimiter
        data.append({'col1': col1, 'col2': col2, 'col3': col3})

# Create a DataFrame from the data list
df = pd.DataFrame(data)

Some usefull tutorials

material can be found in my github

liangch@uef.fi